Hey, everyone! Here are some of the many unblocked game sites I know: LAST UPDATED: 3/22/2026
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| """ | |
| THE WATCHER'S PI-FINE STRUCTURE ALGORITHM - CORRECTED | |
| ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| Fixed version with proper constants and mathematical verification. | |
| """ | |
| from collections import defaultdict | |
| from dataclasses import dataclass, field | |
| from enum import Enum | |
| from functools import lru_cache |
| """ | |
| THE CHAIN-GORR-LIER BOOTSTRAP BULLET ALGORITHM v5.1 | |
| ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ | |
| MULTI-TARGET CHAIN-REACTION, REALITY VISUALIZER & DYNAMIC TELEMETRY | |
| """ | |
| import random | |
| from dataclasses import dataclass, field | |
| from typing import List, Dict, Any | |
| from enum import Enum |
| # Derived from this: https://github.com/gashton/bambustudio_tools/blob/master/bambudiscovery.sh | |
| # Python implementation without need for linux | |
| # Send the IP address of your BambuLab printer to port 2021/udp, which BambuStudio is listens on. | |
| # Ensure your PC has firewall pot 2021/udp open. This is required as the proper response would usually go to the ephemeral source port that the M-SEARCH ssdp:discover message. | |
| # But we are are blindly sending a response directly to the BambuStudio listening service port (2021/udp). | |
| # Temporary solution to BambuStudio not allowing you to manually specify the Printer IP. | |
| # Usage: |
| #!/bin/bash | |
| date -d "$(curl -s --head http://google.com | grep ^Date: | sed 's/Date: //g')" | |
| blueprint: | |
| name: Low battery level detection & notification for all battery sensors | |
| description: Regularly test all sensors with 'battery' device-class for crossing | |
| a certain battery level threshold and if so execute an action. | |
| domain: automation | |
| input: | |
| threshold: | |
| name: Battery warning level threshold | |
| description: Battery sensors below threshold are assumed to be low-battery (as | |
| well as binary battery sensors with value 'on'). |
| へ ♡ ╱|、 | |
| ૮ - ՛) (` - 7 | |
| / ⁻ ៸| |、⁻〵 | |
| 乀 (ˍ, ل ل じしˍ,)ノ | |
| ------------------------------------------------------------------------------------------------------------------------------------------ | |
| /\_/\ (\ __ /) A__A | |
| ( ˶•o•˶) ( •ω• ) ( •⤙• ) | |
| ଘ(ა🍱) (ა🍙૮)。 (🍜٩ )੭ | |
| ------------------------------------------------------------------------------------------------------------------------------------------ | |
| ╱|、 |
As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.
(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))
**1.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.